in-4.6 Conclusion
4 8 12 16 20 24
0 5 10 15 20 25 30 35
Number of SCs N
SC
System Sum Rate (bits/s/Hz)
OMA HetNet NOMA HetNet Exhaustive search Proposed CS-PA-RB
(a)
1 2 3 4 5 6 7 8
5 10 15 20 25 30 35 40 45
Number of allocated SCs per RB, q
max
System Sum Rate (bits/s/Hz)
NSC =10 NSC=20 NSC=30 NSC=40
(b)
Figure 4.6: The performance of the proposed CS-PA-RB versus OMA HetNets and NOMA HetNets in terms of system sum rate at (a) differentNSC values (b) differentqmaxfor different NSC.
terference management algorithm, CS-PA-RB, has been proposed based on CS theory to solve the deduced equivalentl1 norm using the R-WFISTA algorithm for a near-optimum power and RB allocation in HetNets. Simulation results show that the proposed Cr-PA-RB algorithm increases the total sum rate on average by 30% and 25% over conventional OMA and NOMA, respectively, and lower by 7% compared with exhaustive search. Also, the proposed Cr-PA-RB algorithm decreases the outage probabilities on average by 25%
lower than both conventional OMA and NOMA. Moreover, the proposed Cr-PA-RB al-gorithm reduces the complexity on average by 80% compared to the exhaustive search by considering the quadratic time complexity of CS instead of exponential time complexity in the exhaustive search.
4.6 Conclusion
0 5 10 15 20 25 30 35 10−4
10−3 10−2 10−1 100
SNR (dB) Outage Probability of SU i, n, Pout i,n[S b]
OMA HetNet NOMA HetNet Exhaustive search Proposed CS-PA-RB
(a)
0 5 10 15 20 25 30 35 10−5
10−4 10−3 10−2 10−1 100
SNR (dB) Outage Probability of MU n, Pout n[M b]
OMA HetNet NOMA HetNet Exhaustive search Proposed CS-PA-RB
(b)
Figure 4.7: The performance of the proposed CS-PA-RB versus OMA HetNets and NOMA HetNets in terms of (a) outage probability of the SUi,n, Pout[Si,nb] versus SNR, and(b) outage probability of the MUk,n,Pout[Mk,nb] versus SNR.
0.5 1 1.5 2
10−6 10−5 10−4
Minimum Rate for SU
i,n , r[Sb]
i,n , (bits/s/Hz) Outage Probability of SU i,n , Pout i,n[S b]
OMA HetNet NOMA HetNet Exhaustive search Proposed CS-PA-RB
(a)
0.5 1 1.5 2
10−4 10−3
Minimum Rate for MU
k,n , r[Mb]
k,n, (bits/s/Hz) Outage Probability of MU n , Pout n[M b]
OMA HetNet NOMA HetNet Exhaustive search Proposed CS-PA-RB
(b)
Figure 4.8: The performance of the proposed CS-PA-RB versus OMA HetNets and NOMA HetNets in terms of (a) outage probability of the SUi,n, Pout[Si,nb], versus r[Si,nb], and (b) the outage probability of the MUk,n,Pout[Mk,nb], versusr[Mk,nb].
4.6 Conclusion
20 24 28 32 36 40
Number of SCs N
SC
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45
Jain's fairness index
OMA HetNet CS-PA, qmax=1 CS-PA, qmax=2 CS-PA, qmax=3
Figure 4.9: The Jain’s fairness index versus the nubmer os SCs, NSC, for different qmax values.
Chapter 5
Conclusion and Future Works
In this thesis, we studied the application of MIMO-NOMA HetNets in the wireless com-munication system to increase its capacity toward the future sustainable society. We analyzed the interference problem in MIMO-NOMA HetNets, which is considered the main limitation towards harvesting the potentials of MIMO-NOMA HetNets. Also, we explained the main three approaches regarding the IM in MIMO-NOMA HetNets, i.e., beamforming, power control, and resource allocation. Then, we proposed three effective interference management techniques based on theses approaches as follows:
• For the first approach regarding the beamforming based IM in MIOM-NOMA Het-Nets, we proposed the CrIA-CB algorithm (i.e., Chapter 2), where the conventional IA-CB is extended to exploit the degrees of freedom in MIMO to cancel the cross-tier interference in MIMO-NOMA HetNets. The design of the precoding and post-coding vectors is discussed for both single and multiple MC scenarios. Simulation results showed that the proposed CrIA-CB algorithm improved the total sum rate on average by 60%, and decreased the outage probabilities on average by 20% in comparison with the conventional schemes as long as the number of antennas at BSs side is enough to completely cancel the interference.
• For the second approach regarding the PA-based IM in MIMO-NOMA HetNets, we proposed a game theory-based IM algorithm (PA-IA-CB, i.e., Chapter 3) to manage the cross-tier interference by adequately allocating the power to the BSs in the MIMO-NOMA HetNets. We modeled the PA problem as a non-cooperative competitive game between the MBS and the SBS to maximize the total sum rate.
Simulation results showed that the proposed PA-IA-CB algorithm increased the overall sum rate on average by 30%, and decreased the outage probabilities by on average by 10% in compared with conventional schemes as long as the cross channel state information (CrCSI) of the worst user is available at a CCU.
• For the third approach regarding the RBs allocation based IM in NOMA HetNets, we proposed a low-complexity CS-based algorithm (CS-PA-RB, i.e., Chapter 4) to exploit the sparsity property of the RBs allocation in NOMA HetNets. By utilizing this sparsity property, the NP-hard problem is relaxed into an equivalent l1 norm problem for a near-optimum solution. Also, we proposed RWFISTA based CS algorithm on solving the relaxedl1 norm problem. Simulation results showed that the proposed Cr-PA-RB algorithm increases the total sum rate on average by 25%
over conventional schemes as long as the CSI and CrCSI information of all users are available at a CCU. Moreover, the proposed Cr-PA-RB algorithm reduces the complexity on average by 80% compared to the exhaustive search by considering the quadratic time complexity of CS instead of exponential time complexity in the exhaustive search.
Moreover, we can conclude that the choice of the utilized algorithm is based on the system requirements and limitations. For example, PA-IA-CB can be used when the number of MBS antennas is relatively low, while CrIA-CB can be used when the number of the available antennas is high enough to cancel the interference. Also, the CS-PA-RB technique can be utilized to manages the co-tier interference instead of IA-CB for the case of the insufficient number of antennas at the BSs side. In addition, although the proposed techniques are applied for two-tier HetNets, they can be extended to multi-tier HetNets. Moreover, the capacity improvement provided by the proposed techniques will incorporate in the ability of the cellular system to accommodate more users, improve the quality of the provided services, and accept new high-data-rate applications, which improve the human life and participate in economic growth towards the future sustainable society.
On the other hands, although we have to keep pace with the explosive demand for high data rates towards a sustainable society, the data rates growth will result in increasing the consumed energy (energy per bit). Thus, a tradeoff exists between improving energy efficiency and increasing data rates. It is reported that the world’s yearly electricity usage
for the cellular networks sector is rising and expected to reach 51% of global electricity in 2030 unless the energy efficiency (EE) of wireless networks is sufficiently improved.
Also, around 60% of total energy consumption in the cellular network is consumed at the BSs side. Thus, pursuing high-energy efficiency at the BS will be the trend towards the global discipline of “green communication systems”.
In the HetNets cellular system, although the increase in the transmitted power from one of the BSs has a positive effect on the sum rate of its corresponding cell, it affects the other cells negatively through interference as long as increasing the consumed energy. On the other hand, saving the consumed energy by decreasing the transmitted power from the BS may force the subscribers not to reach the minimum required quality of service.
Due to this tradeoff and the high densification of the implemented BSs in HetNets, the power management problem in HetNets is not an easy problem to be solved, and efficient algorithms are required. Thus, in our future works, we aim to study the above energy efficiency and spectrum efficiency dilemma towards a sustainable society through the following directions
1. Profoundly studying the tradeoff relationship between the spectrum efficiency (SE) and energy efficiency (EE) in HetNets.
2. Proposing new techniques for jointly improving SE and EE in HetNets.
3. Proposing new techniques for interference management in HetNets that take energy efficiency into consideration.
LIST OF PUBLICATIONS
• Journals
1. Ahmed Nasser, O. Muta, Maha Elsabrouty, and H. Gacanin, Interference mitigation and power allocation scheme for downlink MIMO-NOMA HetNet, inIEEE Transactions on Ve-hicular Technology, vol. 68, no. 7, pp. 6805 - 6816, July 2019, (Published).
2. Ahmed Nasser, O. Muta, Maha Elsabrouty, and H. Gacanin, Compressive Sensing Based Spectrum Allocation and Power Control for NOMA HetNets, inIEEE Access, vol. 7, no. 7, pp.
98495-98506, July 2019, (Published).
• International Conferences
1. Ahmed Nasser,O. Muta, and M. Elsabrouty, Cross-Tier In-terference Management Scheme for Downlink mMIMIO-NOMA HetNet, in IEEE 89th Vehicular Technology Conference (VTC2019-Spring), Kuala Lumpur, Malaysia, pp. 1-5. April 2019, (Published).
2. Ahmed Nasser, and O. Muta,Performance Analysis of Power Control Based Interference Coordination for Downlink MIMO HetNets, in IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS), Singapore, pp. 1-5. Aug. 2019,
(Published).
REFERENCES
[1] C. V. N. Index, “Global mobile data traffic forecast update, 2016–2021 white paper,”
Cisco: San Jose, CA, USA, 2017.
[2] T. S. Rappaport, W. Roh, and K. Cheun, “Wireless engineers long considered high frequencies worthless for cellular systems. they couldn’t be more wrong,” IEEE SPECTRUM, vol. 51, no. 9, pp. 34–+, 2014.
[3] L. T. Berger, A. Schwager, P. Pagani, and D. Schneider,MIMO power line commu-nications: narrow and broadband standards, EMC, and advanced processing. CRC Press, 2014.
[4] V. Jungnickel, K. Manolakis, W. Zirwas, B. Panzner, V. Braun, M. Lossow, M. Ster-nad, R. Apelfrojd, and T. Svensson, “The role of small cells, coordinated multipoint, and massive MIMO in 5G,”IEEE Communications Magazine, vol. 52, no. 5, pp. 44–
51, 2014.
[5] H. ElSawy, E. Hossain, and M. Haenggi, “Stochastic geometry for modeling, analysis, and design of multi-tier and cognitive cellular wireless networks: A survey,” IEEE Communications Surveys & Tutorials, vol. 15, no. 3, pp. 996–1019, 2013.
[6] A. M. BENAYA, O. MUTA, and M. ELSABROUTY, “Performance analysis of weighted rank constrained rank minimization interference alignment for three-tier downlink heterogeneous networks,” IEICE Transactions on Communications, vol.
E103-B, no. 3, 2020.
[7] Z. Yan, W. Zhou, S. Chen, and H. Liu, “Modeling and analysis of two-tier hetnets with cognitive small cells,” IEEE Access, vol. 5, pp. 2904–2912, 2016.
REFERENCES
[8] J. Zhao, Y. Liu, K. K. Chai, A. Nallanathan, Y. Chen, and Z. Han, “Spectrum allocation and power control for non-orthogonal multiple access in HetNets,” IEEE Transactions on Wireless Communications, vol. 16, no. 9, pp. 5825–5837, 2017.
[9] H. Sari, F. Vanhaverbeke, and M. Moeneclaey, “Multiple access using two sets of orthogonal signal waveforms,”IEEE Communications Letters, vol. 4, no. 1, pp. 4–6, 2000.
[10] S. R. Islam, N. Avazov, O. A. Dobre, and K.-S. Kwak, “Power-domain non-orthogonal multiple access (NOMA) in 5G systems: Potentials and challenges,”
IEEE Communications Surveys & Tutorials, vol. 19, no. 2, pp. 721–742, 2017.
[11] Y. Saito, Y. Kishiyama, A. Benjebbour, T. Nakamura, A. Li, and K. Higuchi, “Non-orthogonal multiple access (NOMA) for cellular future radio access,” in Vehicular Technology Conference (VTC Spring), 2013 IEEE 77th. IEEE, 2013, pp. 1–5.
[12] W. Shin, M. Vaezi, B. Lee, D. J. Love, J. Lee, and H. V. Poor, “Coordinated beamforming for multi-cell MIMO-NOMA,” IEEE Communications Letters, vol. 21, no. 1, pp. 84–87, 2017.
[13] Y. Wu and L. P. Qian, “Energy-efficient NOMA-enabled traffic offloading via dual-connectivity in small-cell networks,” IEEE Communications Letters, vol. 21, no. 7, pp. 1605–1608, 2017.
[14] M. S. Ali, E. Hossain, A. Al-Dweik, and D. I. Kim, “Downlink power allocation for CoMP-NOMA in multi-cell networks,” arXiv preprint arXiv:1801.04981, 2017.
[15] E. Larsson, O. Edfors, F. Tufvesson, and T. Marzetta, “Massive MIMO for next generation wireless systems,” Communications Magazine, IEEE, vol. 52, no. 2, pp.
186–195, 2014.
[16] E. Bj¨ornson, E. G. Larsson, and T. L. Marzetta, “Massive MIMO: Ten myths and one critical question,” arXiv preprint arXiv:1503.06854, 2015.
[17] F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. L. Marzetta, O. Edfors, and F. Tufvesson, “Scaling up mimo: Opportunities and challenges with very large ar-rays,” arXiv preprint arXiv:1201.3210, 2012.
REFERENCES
[18] K. Higuchi and A. Benjebbour, “Non-orthogonal multiple access (noma) with suc-cessive interference cancellation for future radio access,” IEICE Transactions on Communications, vol. 98, no. 3, pp. 403–414, 2015.
[19] T. L. Marzetta, “Noncooperative cellular wireless with unlimited numbers of base station antennas,”IEEE transactions on wireless communications, vol. 9, no. 11, pp.
3590–3600, 2010.
[20] Y. Zhang, Y. Zhu, W. Xia, F. Shen, X. Zuo, F. Yan, and L. Shen, “Game-based power control for downlink non-orthogonal multiple access in HetNets,” pp. 206–212, 2018.
[21] Z. Song, Q. Ni, and X. Sun, “Distributed power allocation for non-orthogonal mul-tiple access heterogeneous networks,” IEEE Communications Letters, vol. 22, no. 3, pp. 622–625, 2018.
[22] A. Nasser, O. Muta, M. Elsabrouty, and H. Gacanin, “Interference mitigation and power allocation scheme for downlink MIMO-NOMA HetNet,” IEEE Transactions on Vehicular Technology, pp. 1–1, 2019.
[23] N. Zhao, F. R. Yu, M. Jin, Q. Yan, and V. C. Leung, “Interference alignment and its applications: A survey, research issues, and challenges,” IEEE Communications Surveys & Tutorials, vol. 18, no. 3, pp. 1779–1803, 2016.
[24] C. Suh, M. Ho, and D. N. Tse, “Downlink interference alignment,” IEEE Transac-tions on CommunicaTransac-tions, vol. 59, no. 9, pp. 2616–2626, 2011.
[25] Y. Liu, Z. Qin, M. Elkashlan, A. Nallanathan, and J. A. McCann, “Non-orthogonal multiple access in large-scale heterogeneous networks,” IEEE Journal on Selected Areas in Communications, vol. 35, no. 12, pp. 2667–2680, 2017.
[26] L. Lei, D. Yuan, C. K. Ho, and S. Sun, “Joint optimization of power and channel allocation with non-orthogonal multiple access for 5G cellular systems,” in Global Communications Conference (GLOBECOM), 2015 IEEE. IEEE, 2015, pp. 1–6.
[27] G. Liu, M. Sheng, X. Wang, W. Jiao, Y. Li, and J. Li, “Interference alignment for partially connected downlink MIMO heterogeneous networks,” IEEE Transactions on Communications, vol. 63, no. 2, pp. 551–564, 2015.
REFERENCES
[28] G. Liu, M. Sheng, X. Wang, Y. Li, and J. Li, “Joint interference alignment and avoidance for downlink heterogeneous networks,” IEEE Communications Letters, vol. 18, no. 8, pp. 1431–1434, 2014.
[29] Z. Ding, F. Adachi, and H. V. Poor, “The application of MIMO to non-orthogonal multiple access,” IEEE Transactions on Wireless Communications, vol. 15, no. 1, pp. 537–552, 2016.
[30] D. Ni, L. Hao, Q. T. Tran, and X. Qian, “Power allocation for downlink NOMA heterogeneous networks,” IEEE Access, vol. 6, pp. 26 742–26 752, 2018.
[31] X. Chen, F.-k. Gong, G. Li, H. Zhang, and P. Song, “User pairing and pair scheduling in massive MIMO-NOMA systems,” IEEE Communications Letters, vol. 22, no. 4, pp. 788–791, 2018.
[32] S. Ali, E. Hossain, and D. I. Kim, “Non-orthogonal multiple access (NOMA) for downlink multiuser MIMO systems: User clustering, beamforming, and power allo-cation,” IEEE Access, vol. 5, pp. 565–577, 2017.
[33] B. Wang, Y. Wu, and K. R. Liu, “Game theory for cognitive radio networks: An overview,” Computer networks, vol. 54, no. 14, pp. 2537–2561, 2010.
[34] M. S. Ali, E. Hossain, A. Al-Dweik, and D. I. Kim, “Downlink power allocation for CoMP-NOMA in multi-cell networks,” IEEE Transactions on Communications, vol. 66, no. 9, pp. 3982–3998, 2018.
[35] L. Lei, E. Lagunas, S. Chatzinotas, and B. Ottersten, “NOMA aided interference management for full-duplex self-backhauling hetnets,” IEEE Communications Let-ters, vol. 22, no. 8, pp. 1696–1699, 2018.
[36] S. Zhang, N. Zhang, G. Kang, and Z. Liu, “Energy and spectrum efficient power allocation with NOMA in downlink HetNets,” Physical Communication, vol. 31, pp.
121–132, 2018.
[37] F. Nikjoo, A. Mirzaei, and A. Mohajer, “A novel approach to efficient resource alloca-tion in NOMA heterogeneous networks: Multi-criteria green resource management,”
Applied Artificial Intelligence, vol. 32, no. 7-8, pp. 583–612, 2018.
REFERENCES
[38] H. Zhang, D.-K. Zhang, W.-X. Meng, and C. Li, “User pairing algorithm with SIC in non-orthogonal multiple access system,” in Communications (ICC), 2016 IEEE International Conference on. IEEE, 2016, pp. 1–6.
[39] S.-M. Guo, C.-C. Yang, P.-H. Hsu, and J. S.-H. Tsai, “Improving differential evo-lution with a successful-parent-selecting framework,” IEEE Transactions on Evolu-tionary Computation, vol. 19, no. 5, pp. 717–730, 2015.
[40] M. S. Ali, H. Tabassum, and E. Hossain, “Dynamic user clustering and power allo-cation for uplink and downlink non-orthogonal multiple access (NOMA) systems,”
IEEE Access, vol. 4, pp. 6325–6343, 2016.
[41] E. J. Cand`es and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag., vol. 25, no. 2, pp. 21–30, 2008.
[42] W. A. Al-Hussaibi and F. H. Ali, “Efficient user clustering, receive antenna selection, and power allocation algorithms for massive MIMO-NOMA systems,” IEEE Access, 2019.
[43] W. U. Khan, Z. Yu, S. Yu, G. A. S. Sidhu, and J. Liu, “Efficient power allocation in downlink multi-cell multi-user NOMA networks,” IET Communications, vol. 13, no. 4, pp. 396–402, 2018.
[44] A. Celik, M.-C. Tsai, R. M. Radaydeh, F. S. Al-Qahtani, and M.-S. Alouini, “Dis-tributed cluster formation and power-bandwidth allocation for imperfect NOMA in DL-HetNets,”IEEE Transactions on Communications, vol. 67, no. 2, pp. 1677–1692, 2019.
[45] Y. Gu, W. Saad, M. Bennis, M. Debbah, and Z. Han, “Matching theory for future wireless networks: fundamentals and applications,” IEEE Communications Maga-zine, vol. 53, no. 5, pp. 52–59, 2015.
[46] J. A. Tropp, A. C. Gilbert, and M. J. Strauss, “Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit,” Signal Processing, vol. 86, no. 3, pp. 572–
588, 2006.
REFERENCES
[47] A. Beck and M. Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM journal on imaging sciences, vol. 2, no. 1, pp. 183–
202, 2009.
[48] A. Nasser, M. Elsabrouty, and O. Muta, “Weighted fast iterative shrinkage thresh-olding for 3D massive MIMO channel estimation,” in Personal, Indoor, and Mobile Radio Communications (PIMRC), 2017 IEEE 28th Annual International Symposium on. IEEE, 2017, pp. 1–5.
[49] K. Lee, Y. Bresler, and M. Junge, “Subspace methods for joint sparse recovery,”
IEEE Trans. Inf. Theory, vol. 58, no. 6, pp. 3613–3641, 2012.
[50] R. K. Jain, D.-M. W. Chiu, and W. R. Hawe, “A quantitative measure of fairness and discrimination,” Eastern Research Laboratory, Digital Equipment Corporation, Hudson, MA, 1984.